{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "housing_df = pd.read_csv('HousingData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "# Set up seaborn dark grid\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(housing_df['MEDV'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(housing_df['MEDV'])\n",
    "plt.title('Median Boston Housing Prices')\n",
    "plt.xlabel('1980 Median Value in Thousands')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Median Boston Housing Prices'\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(housing_df['MEDV'])\n",
    "plt.title(title, fontsize=15)\n",
    "plt.xlabel('1980 Median Value in Thousands')\n",
    "plt.ylabel('Count')\n",
    "plt.savefig(title, dpi=300)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
